brand-pptx
Brand-aware PowerPoint engine. Use to (1) EXTRACT a company's brand from a .pptx template into a reusable "Brand Profile", (2) COMPREHEND the template with the…
Brand-aware Word engine. Use to (1) EXTRACT a company's brand from a Word template into a reusable "Brand Profile", (2) COMPREHEND the template with the model (optional), (3) VERIFY it, (4) GENERATE new on-brand .docx documents FROM a saved profile. Trigger on "extract our
$ npx -y skills add ferdinandobons/brand-docs --skill brand-docx --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/brand-docxContext preview
The summary Claude sees to decide when to auto-load this skill.
Brand-aware Word engine. Use to (1) EXTRACT a company's brand from a Word template into a reusable "Brand Profile", (2) COMPREHEND the template with the model (optional), (3) VERIFY it, (4) GENERATE new on-brand .docx documents FROM a saved profile. Trigger on "extract our
name: brand-docx description: >- Brand-aware Word engine. Use to (1) EXTRACT a company's brand from a Word template into a reusable "Brand Profile", (2) COMPREHEND the template with the model (optional), (3) VERIFY it, (4) GENERATE new on-brand .docx documents FROM a saved profile. Trigger on "extract our brand", "learn/match this template", "use our brand kit", "generate a branded report from our profile", or when a ./brand-kit exists. For one-off Word edits with NO saved brand profile, use the docx skill instead. NOT for .pptx (brand-pptx), .xlsx (brand-xlsx), or PDFs.
Use this skill when the user wants a reusable Word brand kit or wants to create a new on-brand `.docx` from a company template and variable content.
This is an AI-agent skill for Codex and Claude Code. The user should not need to write JSON or run shell commands. The agent converts the user's content into an IntermediateDocument, invokes the internal engine, verifies the output, and returns the generated `.docx`.
Every brand skill (`brand-docx`, `brand-pptx`, `brand-xlsx`) implements the same contract. The deterministic core is **extract / verify / generate**; on top of it sit the optional learning verbs **comprehend / learn / propose-overrides / refine**, each fail-closed (the engine validates every proposal and authors every value).
| Verb | Input | Output | |---|---|---| | **extract** | a company `.docx` template | a reusable Brand Profile | | **comprehend** *(optional, model-driven)* | a saved profile + a model-authored `comprehension.json` | the profile with a validated, cached `comprehension` block | | **verify** | a saved Brand Profile | QA findings + a verdict | | **generate** | content (an IntermediateDocument) + a profile | a new on-brand `.docx` | | **learn** *(deterministic distillation)* | the profile's cross-run generation history | recurring QA findings distilled into shell-frozen overrides, advisory until `--accept` | | **propose-overrides** *(model-driven)* | the recurring remainder `learn` could not bind + a model-authored proposal | shell-backed corrections through the same fail-closed sink, advisory until `--accept` | | **refine** | end-of-generation user feedback (text or a screenshot) as a `refinement.json` delta | the existing comprehension overlaid for FUTURE generations, advisory until `--accept` |
`comprehend` is **optional**: `generate` works on the deterministic profile alone. When a current comprehension is present, `generate` additionally reconciles the template's preserved cover/index structures with the new content. See [reference/comprehension.md](reference/comprehension.md) for the full step.
Before doing any work, run:
python scripts/cli.py doctor
Use its output to decide the run mode:
or generation; the core engine is not ready.
optional PyMuPDF/`fitz`), the core L0 workflow can still run, but a full visual audit cannot be claimed. Tell the user what is missing, include the install/repair hint printed by `doctor`, and either proceed with degraded QA or install the renderer first.
rendered residual-text proof is incomplete. Report that limitation when judging stale placeholders or field caches.
generation. If the environment cannot run them, `deep` generates a degraded manifest and `strict` fails with a visual proof blocker.
1. Run the dependency preflight above and report any degraded capability. 2. Determine the brand name and locate the user-provided `.docx` template. 3. If no matching `brand-kit/<name>` exists, **extract** one. 4. **Comprehend** the template (optional, model-driven; see below). Skip when a current comprehension is already cached or no model is available. 5. Convert the user's requested content into `IntermediateDocument` JSON. 6. **Generate** the `.docx` with the internal engine. 7. Run **QA** and report any warnings honestly. 8. **Feedback** (only after returning the file): invite a refinement of the understanding for future documents (see below).
Before generation, inspect `profile.json.artifact_catalog` when the user asks to mimic a specific template piece. It records OOXML parts, media parts, paragraph/table styles, style details, sections/margins, paragraph samples, and table counts.
The IDoc is where "correct document" becomes "great document". Author it role-first, against the profile, never style-first:
1. **Read `brand-kit/<name>/PROFILE.md` before writing a block.** It lists the role
BrandDocs is a set of agent skills that learn your existing Word, PowerPoint and Excel templates and generate new on-brand documents from them. Unlike generic AI document generators, it preserves brand, structure, styles and formulas by construction.
Brand-aware PowerPoint engine. Use to (1) EXTRACT a company's brand from a .pptx template into a reusable "Brand Profile", (2) COMPREHEND the template with the…
Brand-aware Excel engine. Use to (1) EXTRACT a company's brand from a .xlsx template into a reusable "Brand Profile", (2) COMPREHEND the template with the…